Model Compression

Model compression refers to techniques that reduce the size of machine learning models while maintaining their performance. This process is crucial for deploying models in resource-constrained environments, such as mobile devices or edge computing. Key characteristics include reducing the number of parameters, quantization, and pruning, which help in speeding up inference times and decreasing memory usage. Common use cases include deploying deep learning models for mobile applications, improving the efficiency of AI systems in real-time applications, and enabling faster model updates without sacrificing accuracy.